Automatic ranking of design parameter significance for fast and accurate cae-based design space exploration using parameter sensitivity feedback

ABSTRACT

A computer-implemented method for ranking design parameter significance includes a computer receiving an input dataset representative of a physical object. This input dataset includes a baseline parameters and associated probabilities. The computer also receives performance requirements. For each respective baseline parameter, the computer performs an analysis process. During this analysis process, a range of parameter values are selected for the respective baseline parameter based on its corresponding probability distribution. The range of parameter values are segmented into parameter subsets and multiple instances of a simulation are executed using the performance requirements to yield snapshots. A Proper Orthogonal Decomposition (POD) basis is derived using the snapshots. A sensitivity analysis is performed based on the POD basis to yield a sensitivity measurement representative of an effect of variation of the respective parameter on the performance requirements. The computer may then generate a ranking of the baseline parameters according to their corresponding sensitivity measurements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional application Ser.No. 62/090,412 filed Dec. 11, 2015, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present invention relates generally to methods, systems, andapparatuses for the automatic ranking of design parameter significancefor fast and accurate Computer-aided Engineering (CAE)-based designspace exploration using parameter sensitivity feedback. The disclosedtechniques may be applied, for example, to facilitate the selection ofoptimal design parameters for products subject to complex physicalmodels.

BACKGROUND

Design space exploration using Computer-aided Engineering (CAE) analysesgreatly benefits industrial product development. This is typically aniterative process involving a designer varying a set of parameters, andultimately converging to an optimal set that achieves good expectedfunctional characteristics as reported by the CAE analyses. However, theextent of CAE-based design exploration is currently limited due to thefact that products (such as aircrafts) are typically defined by a largenumber of design parameters, and computing CAE responses for a singleset is a time consuming process thereby prohibiting the use of CAE for alarge set of design variations.

Computational Fluid Dynamics (CFD) is an example of a tool employed inthe iterative CAE process. CFD uses numerical methods and algorithms tosolve and analyze problems that involve fluid flows. Performing CFDsimulations is a time-consuming and performance-intensive activity.Additionally, CFD model preparation (including preparation of CFD mesh)is a very time-consuming and highly operator dependent task, oftenconstituting the bottle neck of the process. This is often the result oftedious geometry cleanup and preparation, and challenges associated withobtaining a closed fluid volume. Repeating this process for eachpossible variation of a geometrical model becomes very computationallyexpensive. Thus, it would be valuable to have a reliable method toautomatically identify the points in the parameter space that need to beanalyzed in detail.

To date, the most direct and computationally expensive approach toevaluate design variations of a computer aided design (CAD) model is thegeneration of a detailed computational model and CFD simulation forseveral variations of all the considered parameters. This approach, ifperformed with a fine granularity of the design parameter-space sweep,heuristically allows the selection of a good approximation of theoptimal design to achieve the desired technical performance. However,the realization of this approach becomes intractable in realapplications. To address this issue, generally in practice, coarsegranularity of parameter space is chosen. In addition,designers/engineers use their experience and comparison with past casesto manually reduce the features to explore. Often, in combination withthis, approaches to reduce the computational cost of each CFDsimulations are also implemented. This includes the use ofhigh-performance computing, the use of reduced order modeling, and thecombined use of reduced-order and full-order CFD simulation techniques.

SUMMARY

Embodiments of the present invention address and overcome one or more ofthe above shortcomings and drawbacks, by providing methods, systems, andapparatuses related to the automatic ranking of design parametersignificance, for fast and accurate CAE-based design space exploration,using parameter sensitivity feedback. Briefly, the technology describedherein utilizes model reduction techniques to analyze high-dimensionaldynamical systems using lower-dimensional approximations, whichreproduces the characteristic dynamics of the system. Using theseapproximations, an understanding of the effects of different parameterson design requirements can be developed while minimizing computationalcost and storage requirements. Parameters may then be ranked as highlysignificant if a metric (or combination of metrics) of interest ishighly sensitive to that parameter.

According to some embodiments, a computer-implemented method for rankingdesign parameter significance includes a computer receiving an inputdataset representative of a physical object, wherein the input datasetcomprises a plurality of baseline parameters and a probability for eachof the plurality of baseline parameters. The computer also receives oneor more performance requirements. In some embodiments, the input datasetcomprises a geometry dataset representative of the physical object(e.g., a Computer Aided Design (CAD) dataset). In one embodiment, thegeometry dataset comprises a realization of the physical object and theprobability distribution for each parameter is determined based on ananalysis of pre-existing alternative realizations of the physicalobject. The probability distribution for each parameter may be defined,for example, a priori, based on one or more characteristics of theplurality of baseline parameters. In some embodiments, the input datasetmay include one or more non-geometric datasets related to the physicalobject.

Continuing with reference to the method for ranking design parametersignificance, after receiving the input dataset, the computer performsan analysis process for each respective baseline parameter included inthe plurality of baseline parameters. During this analysis process, arange of parameter values are selected for the respective baselineparameter based on its corresponding probability distribution. The rangeof parameter values are segmented into a plurality of parameter subsetsbased on a pre-determined granularity for the respective parameter. Aplurality of instances of a simulation is executed using the one or moreperformance requirements to yield a plurality of snapshots, wherein eachrespective instance corresponds to one of the plurality of parametersubsets. Next, a Proper Orthogonal Decomposition (POD) basis is derivedon the plurality of snapshots. A sensitivity analysis may then beperformed based on the POD basis to yield a sensitivity measurementrepresentative of an effect of variation of the respective parameter onthe one or more performance requirements. In some embodiments, theextent of deviation of the respective parameter from its correspondingbaseline parameter value is determined during the analysis process. Ifthe extent of deviation is greater than a predetermined threshold value,the pre-determined granularity for the respective parameter is reducedand the analysis process is repeated for the respective parameter. Oncethe analysis process is complete, the computer may then generate aranking of the plurality of baseline parameters according to theircorresponding sensitivity measurements. In some embodiments, a thresholdis applied to the ranking of the plurality of baseline parameters toidentify a plurality of highest ranking parameters.

According to other embodiments, an article of manufacture for rankingdesign parameter significance includes a non-transitory, tangiblecomputer-readable medium holding computer-executable instructions forperforming the aforementioned method, with or without the additionalfeatures set forth above.

According to other embodiments, a system for ranking design parametersignificance includes an input module, a simulation module, a modelreduction module, a sensitivity module, and a ranking module. The inputmodule is configured to determine a probability for each of a pluralityof baseline parameters included in an input dataset representative of aphysical object. The simulation module is configured to perform ananalysis process for each respective baseline parameter included in theplurality of baseline parameters. This analysis process includesselecting a range of parameter values for the respective baselineparameter based on its corresponding probability distribution,segmenting the range of parameter values into a plurality of parametersubsets based on a pre-determined granularity for the respectiveparameter, and running a plurality of instances of a simulation usingone or more performance requirements associated with the physical objectto yield a plurality of snapshots. The model reduction module isconfigured to derive a plurality of POD bases using snapshots generatedby the simulation module. The sensitivity module is configured toperform a sensitivity analysis based on the POD basis to yield asensitivity measurement for each respective baseline parameter includedin the plurality of baseline parameters. Each respective sensitivitymeasurement is representative of an effect of variation of therespective baseline parameter on the one or more performancerequirements. Finally, the ranking module is configured to generate aranking of the plurality of baseline parameters according to theircorresponding sensitivity measurements. In some embodiments, the systemfurther includes a parallel computing architecture comprising aplurality of processors configured to execute the plurality of instancesof the simulation in parallel.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 provides an overview of a system that a user may apply in someembodiments to analyze and rank a plurality of parameters based on theeffect of their variation on design metrics;

FIG. 2 provides an example of a method for automatically ranking thesignificance of the design parameters using concepts described abovewith respect to FIG. 1; and

FIG. 3 illustrates an exemplary computing environment within whichembodiments of the invention may be implemented.

DETAILED DESCRIPTION

The following disclosure describes the present invention according toseveral embodiments directed at methods, systems, and apparatusesrelated to the automatic ranking of design parameter significance forthe exploration of CAE-based design spaces using parameter sensitivityfeedback. For example, in some embodiments of the present invention,techniques are used to reduce the computational cost of parametricdesign analysis by taking into account the probability distribution ofthe given variations in the design parameters, and the impact of thesevariations on the considered performance metrics. Similar techniques canbe applied to quantify the effect of inherent uncertainty on features(e.g., geometrical features, boundary conditions, etc.) on theperformance metrics. For example, techniques described herein may beused to address the effect of manufacturing defects on designrequirements.

FIG. 1 provides an overview of a system 100 that a user may apply insome embodiments to analyze and rank a plurality of parameters based onthe effect of their variation on design metrics. The user may be, forexample, a design engineer analyzing a design of a complex physicaldevice such as an aircraft wing. Such a device may have millions, if notbillions, of different design parameters. Thus, as an initial task, theuser must determine which parameters have the most impact on systemperformance. In this way, the user can determine which parameters tofocus resources on for additional analysis.

To analyze the parameter space, the User Computer 105 connects to aParameter Selection Computer 115 via a Network 110. The Network 110 cangenerally be any computer network generally known in the art. Thus, insome embodiments, the User Computer 105 connects over a wired orwireless local area network to the Parameter Selection Computer 115. Inother embodiments, the Parameter Selection Computer 115 may beimplemented in a location remote form the location of the User Computer105. For example, the Parameter Selection Computer 115 can beimplemented using a “cloud computing” architecture model which allowsthe User Computer 105 to connect via the Internet.

Various interfaces may be used to facilitate the communications betweenthe User Computer 105 and the Parameter Selection Computer 115. Forexample, in some embodiments the Parameter Selection Computer supports aweb application which displays a graphical user interface (GUI) in awebpage on the User Computer 105. The User can then interact with theParameter Selection Computer 115. In other embodiments, the ParameterSelection Computer 115 may be configured to accept commands via a customapplication programming interface (API). Thus, for example, adevelopment tool installed on the User Computer 105 may be configured touse the API in parameter selection.

Parameter Selection Computer 115 provides an automated way of analyzingthe design parameter space to identify the parameters that are mostrelevant to the set of design requirements. The Input Module 115A isconfigured to receive a computer aided design (CAD) geometry dataset ofone realization of product being designed. The received geometryincludes a combination of geometric parameters (herein referred to asthe “baseline geometry”) and a probability distribution for eachparameter. The Input Module 115A additionally receives one or moreperformance metric(s) defining the user's requirements for the design ofthe physical object. These requirements may be specified, for example,in a data file formatted in any general file format known in the art(e.g., Extensible Markup Language). Examples of performance metrics maybe mechanical properties (e.g., deformation in particular areas of theobject), electrical (e.g., energy efficiency), or even metrics notdirectly related to the physical object itself (e.g., cost tomanufacture).

Continuing with reference to FIG. 1, a Simulation Module 115B creates arange of values for geometric parameters and runs a simulation for eachdistinct value of each parameter, according to a predefineddistribution. The type of simulation executed by the Simulation Module115B may vary depending on the physical object being analyzed. Forexample, for an airplane wing, a CFD simulation may be employed.Additionally, in some embodiments, the Simulation Module 115B isconfigured to execute multiple simulations. Continuing with the exampleof the airplane wing, in addition to the CFD simulation, the SimulationModule 115B may execute a simulation of the thermal dynamics of the wingunder certain conditions.

Each execution of the simulation by the Simulation Module 115B producesa “snapshot” of the system state. Because the input parameter to thesimulation is the only parameter being varied from the baselinegeometry, the difference between each snapshot is based only on thedifference in the value of the input parameter. Also, note that thenumber of snapshots will be depending on the sampling rate of the inputparameter. Thus, the more granular the sampling, the more snapshots aregenerated. The Simulation Module 115B aggregates the snapshots to createa matrix representative of the effect of the variation on the parametervalue on the system metrics.

The Model Reduction Module 115C builds a reduced order model of thesimulation results. The model may be built using any reduced ordermethodology known in the art. For example, in some embodiments, ProperOrthogonal Decomposition (POD) techniques are employed. As is understoodin the art, POD is a model reduction technique that identifies thesystem mode and the corresponding coefficients for each input sample ina dataset in order to develop a reduced order model representative ofthe original system. The snapshot matrix is decomposed into a set ofbasis vectors using POD. The snapshots are projected onto the new basis.Then, system behavior can be predicted from these projected coordinatesand the basis vectors. It should be noted that the basis vectorsproduced by POD are ordered in decreasing order of importance. Thisallows users to selectively discard lower order modes to reduce modelsize while maintaining a highly accurate estimate of the originalsystem. The accuracy of the model is generally referred to by the numberof modes retained after the discarding process. Thus, a k-orderapproximation of the system only retains the first k modes.

The Sensitivity Analysis Module 115D performs an analysis of the system,as defined by the POD basis, to determine how sensitive each metric isto variations of the parameter of interest in comparison to its baselinevalue. There are many techniques of performing a sensitivity analysisgenerally known in the art and, in principle, these techniques may beapplied by the Sensitivity Analysis Module 115D. For example, in someembodiments, analysis is performed on the derivatives of the modesgenerated by the Model Reduction Module 115C. The output of thesensitivity analysis is a value or a set of values which provides anindication of how sensitive the system is to perturbations of theparticular parameter. For example, in some embodiments, a distributionis generated by the sensitivity analysis and the variance of thedistribution is used as the sensitivity measurement for that particularparameter. In other embodiments different techniques may be applied tocapture the variability information. In addition to the range ofvariation produced by the given variability of a particular parameter,the Sensitivity Analysis Module 115D may also provide relatedinformation such as the trend and extent of deviation from baselineperformance metric values for a given deviation of the parameter fromthe baseline geometry.

The process described above with respect to the Simulation Module 115Band the sensitivity analysis is repeated for each parameter in theparameter space. It should be noted that the granularity of values usedfor each parameter may be different. For example, based on a prioriinformation, a user may specify that certain parameters are moreimportant than others. The Parameter Selection Computer 115 may thenselect the values of such parameter for the simulation based on theirrelative significance. Alternatively, the granularity of the parameterspace, for each parameter, can be derived as output of the SensitivityAnalysis Tool, based on the amount of variation that the user wants todetect in the CAE response.

The Ranking Module 115E ranks each parameter based on its sensitivitymeasurement provided by the Sensitivity Analysis Module 115D. Thisranking results in a list of the parameters ordered based on the effectof their variation on the performance metrics.

The Output Module 115F provides the ranked results in a user-readableformat. For example, in some embodiments, the Parameter SelectionComputer 115 provides the ranked results in a GUI, with or without anindication of the individual ranking of each parameter. In someembodiments, the Output Module 115F allows the user to specify athreshold for the ranked results in order to reduce the parameter spaceby a given dimension. It should be noted that the output step isoptional. For example, in some embodiments, the functionality of theParameter Selection Computer 115 may be part of a general design toolwith additional functionality. The parameter selection process can be,in a sense transparent, from the user. For example, upon beginning a newproduct design, the tool may automatically analyze the parameter spaceand direct the user to perform additional analysis on the highest rankedparameters, according to a predefined threshold.

The separation of the User Computer 105 and the Parameter SelectionComputer 115, as illustrated in FIG. 1, is one example of how thevarious systems and techniques described herein may be implemented. Inother embodiments, for example, the functionality of the ParameterSelection Computer 115 may be implemented directly in an applicationinstalled on the User Computer 105, thus eliminating the need forseparate physical device. Additionally, in some embodiments, variouscomponents of the system 100 are executed on a parallel computingplatform comprising of one or more graphical processing units (GPUs).For example, in one embodiment, a parallel computing platform isconfigured to execute multiple instances of the simulation in parallel.Any parallel computing platform generally known in the art may be usedto facilitate parallel execution and programming models, including theNVIDIA™ Compute Unified Device Architecture (CUDA) may be used tooptimize usage of the processing units in simulation computations.

FIG. 2 provides an example of a method 200 for automatically ranking thesignificance of the design parameters using concepts described abovewith respect to FIG. 1. Briefly, the method 200 evaluates the designspace by two automatic operations. First, the design parameters areranked according to significance and, secondly, the granularity of thesweep of the multidimensional design parameter space is refined. Thisapproach allows a user to explore design variations of a CAD geometrydataset to be manufactured on the basis of their physical performance.For example, in the context of the design of an aircraft wing, thephysical performance may be the response of the wing to external flowsurrounding it (e.g., the aerodynamic forces experienced by an aircraftduring operation). Additionally, the most relevant design parameters fordesign analysis may be optimally selected without compromising theaccuracy of the analysis.

The example of FIG. 2 illustrates how the method 200 could be applied inthe context of an airplane design scenario. The geometric designparameters may include, for example, the width of the wing of theaircraft at the point of attachment with the fuselage, the length of thewing, the position of the engine. However, it should be noted that thegeneral algorithm illustrated in FIG. 2 may be applied to any designcontent in which a physical property of a geometric model can beanalyzed through simulation techniques.

Starting at step 205, a dataset with the CAD geometry of one realizationof the airplane is received. The dataset includes a given combination ofgeometric parameters called baseline geometry, and a probabilitydistribution for each parameter. This probability distribution may bedefined, for example, a priori, based on the design space to beexplored. Alternatively, the probability distribution may be derivedfrom the analysis of existing realizations of the object (e.g.,analyzing the geometry of several CAD models of aircrafts). The numberof parameters to explore may be denoted as NP: P₁ . . . P_(NP). Next, atstep 210, one or more performance metrics are defined based on theuser's requirements. This number is denoted with NM: M:₁ . . . M_(NM).

Continuing with reference to FIG. 2, at steps 215-225, the variabilityof the performance metrics in terms of variability of P₁ . . . P_(NP) isquantified. In the example of FIG. 2, this quantification is achieved byperforming a sensitivity analysis based on reduced-order model(“reduced-order sensitivity analysis”), utilizing a technique such asPOD. As discussed above with respect to FIG. 1, POD is a conventionaldata analysis technique which obtains low-dimensional approximatedescriptions of high-dimensional processes. At step 215, N_(s) CFDsimulations are separately run for each parameter, including onesimulation for the baseline geometry. Each simulation corresponds to adifferent value of the particular parameter being analyzed and the totalnumber of simulations, N_(s), will depend on the granularity of how theparameter is sampled. N_(s) is intended to be significantly smaller thanthe number of parameter variations needed for a thorough parameter sweepfor each parameter P_(i). Each simulation executed at step 215 resultsin a snapshot of data. At step 220, these snapshots are used to derive areduced-order model (e.g., POD) basis. Then, at step 225, reduced-ordermodeling techniques are used to evaluate (M₁ . . . M_(NM)) incorrespondence of the variations on (P₁ . . . P_(NP)) according to theirpredefined distribution.

For each parameter P_(i), the process employed by steps 215-225 willproduce the range of variation on (M₁ . . . M_(NM)) caused by the givenvariability in P_(i). This is used at step 230 to rank the designparameters based on the effect of their variation on the performancemetrics. Once ranked, a threshold may be applied to reduce the designspace to the most relevant parameters.

Additionally, the process employed by steps 215-225 will produce thetrend and extent of deviation from baseline of (M₁ . . . M_(NM)) for agiven deviation on P_(i). from baseline geometry. At step 235, thisinformation is used to redefine (and possibly coarsen) the granularityof the sweep of parameter space for the parameters selected at step 230.In fact, the discretization step of the parameter space sweep of P_(i)can be larger than the minimum step that produces a significantvariation in the performance metrics, where the significance levelshould be defined by the user, based on the specific application.

The method 200 illustrated in FIG. 2 can be varied in differentembodiments. For example, in some embodiments, the parameter space maybe refined within a neighborhood of the best performing points in theparameter space. In other embodiments, the reduced-order sensitivityanalysis performed at step 225 may be recursively applied by addingsnapshots to the POD datasets based on the data generated at step 235.

FIG. 3 illustrates an exemplary computing environment 300 within whichembodiments of the invention may be implemented. For example, thiscomputing environment 300 may be used to implement the method 200described in FIG. 2 as well one or more of the components illustrated inthe system 100 of FIG. 1. The computing environment 300 may includecomputer system 310, which is one example of a computing system uponwhich embodiments of the invention may be implemented. Computers andcomputing environments, such as computer system 310 and computingenvironment 300, are known to those of skill in the art and thus aredescribed briefly herein.

As shown in FIG. 3, the computer system 310 may include a communicationmechanism such as a bus 321 or other communication mechanism forcommunicating information within the computer system 310. The computersystem 310 further includes one or more processors 320 coupled with thebus 321 for processing the information. The processors 320 may includeone or more central processing units (CPUs), graphical processing units(GPUs), or any other processor known in the art.

The computer system 310 also includes a system memory 330 coupled to thebus 321 for storing information and instructions to be executed byprocessors 320. The system memory 330 may include computer readablestorage media in the form of volatile and/or nonvolatile memory, such asread only memory (ROM) 331 and/or random access memory (RAM) 332. Thesystem memory RAM 332 may include other dynamic storage device(s) (e.g.,dynamic RAM, static RAM, and synchronous DRAM). The system memory ROM331 may include other static storage device(s) (e.g., programmable ROM,erasable PROM, and electrically erasable PROM). In addition, the systemmemory 330 may be used for storing temporary variables or otherintermediate information during the execution of instructions by theprocessors 320. A basic input/output system (BIOS) 333 containing thebasic routines that help to transfer information between elements withincomputer system 310, such as during start-up, may be stored in ROM 331.RAM 332 may contain data and/or program modules that are immediatelyaccessible to and/or presently being operated on by the processors 320.System memory 330 may additionally include, for example, operatingsystem 334, application programs 335, other program modules 336 andprogram data 337.

The computer system 310 also includes a disk controller 340 coupled tothe bus 321 to control one or more storage devices for storinginformation and instructions, such as a hard disk 341 and a removablemedia drive 342 (e.g., floppy disk drive, compact disc drive, tapedrive, and/or solid state drive). The storage devices may be added tothe computer system 310 using an appropriate device interface (e.g., asmall computer system interface (SCSI), integrated device electronics(IDE), Universal Serial Bus (USB), or FireWire).

The computer system 310 may also include a display controller 365coupled to the bus 321 to control a display 366, such as a cathode raytube (CRT) or liquid crystal display (LCD), for displaying informationto a computer user. The computer system includes an input interface 360and one or more input devices, such as a keyboard 362 and a pointingdevice 361, for interacting with a computer user and providinginformation to the processor 320. The pointing device 361, for example,may be a mouse, a trackball, or a pointing stick for communicatingdirection information and command selections to the processor 320 andfor controlling cursor movement on the display 366. The display 366 mayprovide a touch screen interface which allows input to supplement orreplace the communication of direction information and commandselections by the pointing device 361.

The computer system 310 may perform a portion or all of the processingsteps of embodiments of the invention in response to the processors 320executing one or more sequences of one or more instructions contained ina memory, such as the system memory 330. Such instructions may be readinto the system memory 330 from another computer readable medium, suchas a hard disk 341 or a removable media drive 342. The hard disk 341 maycontain one or more datastores and data files used by embodiments of thepresent invention. Datastore contents and data files may be encrypted toimprove security. The processors 320 may also be employed in amulti-processing arrangement to execute the one or more sequences ofinstructions contained in system memory 330. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions. Thus, embodiments are not limited to any specificcombination of hardware circuitry and software.

As stated above, the computer system 310 may include at least onecomputer readable medium or memory for holding instructions programmedaccording to embodiments of the invention and for containing datastructures, tables, records, or other data described herein. The term“computer readable medium” as used herein refers to any medium thatparticipates in providing instructions to the processor 320 forexecution. A computer readable medium may take many forms including, butnot limited to, non-volatile media, volatile media, and transmissionmedia. Non-limiting examples of non-volatile media include opticaldisks, solid state drives, magnetic disks, and magneto-optical disks,such as hard disk 341 or removable media drive 342. Non-limitingexamples of volatile media include dynamic memory, such as system memory330. Non-limiting examples of transmission media include coaxial cables,copper wire, and fiber optics, including the wires that make up the bus321. Transmission media may also take the form of acoustic or lightwaves, such as those generated during radio wave and infrared datacommunications.

The computing environment 300 may further include the computer system310 operating in a networked environment using logical connections toone or more remote computers, such as remote computer 380. Remotecomputer 380 may be a personal computer (laptop or desktop), a mobiledevice, a server, a router, a network PC, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to computer system 310. When used in anetworking environment, computer system 310 may include modem 372 forestablishing communications over a network 371, such as the Internet.Modem 372 may be connected to bus 321 via user network interface 370, orvia another appropriate mechanism.

Network 371 may be any network or system generally known in the art,including the Internet, an intranet, a local area network (LAN), a widearea network (WAN), a metropolitan area network (MAN), a directconnection or series of connections, a cellular telephone network, orany other network or medium capable of facilitating communicationbetween computer system 310 and other computers (e.g., remote computer380). The network 371 may be wired, wireless or a combination thereof.Wired connections may be implemented using Ethernet, Universal SerialBus (USB), RJ-11 or any other wired connection generally known in theart. Wireless connections may be implemented using Wi-Fi, WiMAX, andBluetooth, infrared, cellular networks, satellite or any other wirelessconnection methodology generally known in the art. Additionally, severalnetworks may work alone or in communication with each other tofacilitate communication in the network 371.

The embodiments of the present disclosure may be implemented with anycombination of hardware and software. In addition, the embodiments ofthe present disclosure may be included in an article of manufacture(e.g., one or more computer program products) having, for example,computer-readable, non-transitory media. The media has embodied therein,for instance, computer readable program code for providing andfacilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.

A graphical user interface (GUI), as used herein, comprises one or moredisplay images, generated by a display processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions. The GUI also includes anexecutable procedure or executable application. The executable procedureor executable application conditions the display processor to generatesignals representing the GUI display images. These signals are suppliedto a display device which displays the image for viewing by the user.The processor, under control of an executable procedure or executableapplication, manipulates the GUI display images in response to signalsreceived from the input devices. In this way, the user may interact withthe display image using the input devices, enabling user interactionwith the processor or other device.

The functions and process steps herein may be performed automatically orwholly or partially in response to user command. An activity (includinga step) performed automatically is performed in response to one or moreexecutable instructions or device operation without user directinitiation of the activity.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. Asdescribed herein, the various systems, subsystems, agents, managers andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

1. A computer-implemented method for ranking design parametersignificance, the method comprising: receiving, by a computer, an inputdataset representative of a physical object, wherein the input datasetcomprises a plurality of baseline parameters and a probability for eachof the plurality of baseline parameters; receiving, by the computer, oneor more performance requirements; for each respective baseline parameterincluded in the plurality of baseline parameters, using the computer toperform an analysis process comprising: selecting a range of parametervalues for the respective baseline parameter based on its correspondingprobability distribution, segmenting the range of parameter values intoa plurality of parameter subsets based a pre-determined granularity forthe respective parameter, running a plurality of instances of asimulation using the one or more performance requirements to yield aplurality of snapshots, wherein each respective instance corresponds toone of the plurality of parameter subsets, deriving a Proper OrthogonalDecomposition (POD) basis using the plurality of snapshots, performing asensitivity analysis based on the POD basis to yield a sensitivitymeasurement representative of an effect of variation of the respectiveparameter on the one or more performance requirements; and generating,by the computer, a ranking of the plurality of baseline parametersaccording to their corresponding sensitivity measurements.
 2. The methodof claim 1, wherein the input dataset comprises a geometry datasetrepresentative of the physical object.
 3. The method of claim 2, whereinthe geometry dataset comprises a Computer Aided Design (CAD) dataset. 4.The method of claim 2, wherein the input dataset comprises one or morenon-geometric datasets related to the physical object.
 5. The method ofclaim 2, wherein the geometry dataset comprises a realization of thephysical object and the probability distribution for each parameter isdetermined based on an analysis of one or more pre-existing alternativerealizations of the physical object.
 6. The method of claim 1, whereinthe probability distribution for each parameter is defined a prioribased on one or more characteristics of the plurality of baselineparameters.
 7. The method of claim 1, further comprising: applying athreshold to the ranking of the plurality of baseline parameters toidentify a plurality of highest ranking parameters.
 8. The method ofclaim 1, wherein the plurality of parameter subsets for each respectivebaseline parameter are sized according to the pre-determined granularityfor the respective parameter.
 9. The method of claim 8, furthercomprising: determining an extent of deviation of the respectiveparameter from its corresponding baseline parameter value; and if theextent of deviation is greater than a predetermined threshold value,reducing the pre-determined granularity for the respective parameter andrepeating the analysis process for the respective parameter.
 10. Themethod of claim 1, wherein the plurality of instances of the simulationare executed in parallel during the analysis process using a parallelcomputing architecture.
 11. An article of manufacture for ranking designparameter significance, the article of manufacture comprising anon-transitory, tangible computer-readable medium holdingcomputer-executable instructions for performing a method comprising:determining a probability for each of a plurality of baseline parametersincluded in an input dataset representative of a physical object; foreach respective baseline parameter included in the plurality of baselineparameters, performing an analysis process comprising: selecting a rangeof parameter values for the respective baseline parameter based on itscorresponding probability distribution, segmenting the range ofparameter values into a plurality of parameter subsets based apre-determined granularity for the respective parameter, running aplurality of instances of a simulation using one or more performancerequirements associated with the physical object to yield a plurality ofsnapshots, wherein each respective instance corresponds to one of theplurality of parameter subsets, deriving a Proper OrthogonalDecomposition (POD) basis on the plurality of snapshots, performing asensitivity analysis based on the POD basis to yield a sensitivitymeasurement representative of an effect of variation of the respectiveparameter on the one or more performance requirements; and generating aranking of the plurality of baseline parameters according to theircorresponding sensitivity measurements.
 12. The article of manufactureof claim 11, wherein the input dataset comprises a geometry datasetrepresentative of the physical object.
 13. The article of manufacture ofclaim 12, wherein the input dataset comprises one or more non-geometricdatasets related to the physical object.
 14. The article of manufactureof claim 12, wherein the geometry dataset comprises a realization of thephysical object and the probability distribution for each parameter isdetermined based on an analysis of one or more pre-existing alternativerealizations of the physical object.
 15. The article of manufacture ofclaim 11, wherein the method further comprises: defining the probabilitydistribution for each parameter a priori based on one or morecharacteristics of the plurality of baseline parameters.
 16. The articleof manufacture of claim 11, wherein the method further comprises:applying a threshold to the ranking of the plurality of baselineparameters to identify a plurality of highest ranking parameters. 17.The article of manufacture of claim 11, wherein the plurality ofparameter subsets for each respective baseline parameter are sizedaccording to the pre-determined granularity for the respectiveparameter.
 18. The article of manufacture of claim 17, wherein themethod further comprises determining an extent of deviation of therespective parameter from its corresponding baseline parameter value;and if the extent of deviation is greater than a predetermined thresholdvalue, reducing the pre-determined granularity for the respectiveparameter and repeating the analysis process for the respectiveparameter.
 19. A system for ranking design parameter significance, thesystem comprising: an input module configured to: determine aprobability for each of a plurality of baseline parameters included inan input dataset representative of a physical object; a simulationmodule configured to perform an analysis process for each respectivebaseline parameter included in the plurality of baseline parameters,wherein the analysis process comprises: selecting a range of parametervalues for the respective baseline parameter based on its correspondingprobability distribution, segmenting the range of parameter values intoa plurality of parameter subsets based a pre-determined granularity forthe respective parameter, running a plurality of instances of asimulation using one or more performance requirements associated withthe physical object to yield a plurality of snapshots, wherein eachrespective instance corresponds to one of the plurality of parametersubsets; a model reduction module configured to derive a POD basis usingsnapshots generated by the simulation module; a sensitivity moduleconfigured to perform a sensitivity analysis based on the POD basis toyield a sensitivity measurement for each respective baseline parameterincluded in the plurality of baseline parameters, wherein eachrespective sensitivity measurement is representative of an effect ofvariation of the respective baseline parameter on the one or moreperformance requirements; and a ranking module configured to generate aranking of the plurality of baseline parameters according to theircorresponding sensitivity measurements.
 20. The system of claim 19,further comprising: a parallel computing architecture comprising aplurality of processors configured to execute the plurality of instancesof the simulation in parallel.